The aim of this paper is to provide a data-driven approach for modeling of a continuous type
bioreactor. The data sets used for identification are gathered in the presence of various …

This chapter deals with one of the complex optimization problems known as system
identification. The concept of the system identification has recently gained a considerable …

Since ancient times, humans have used rules of thumb and strategies extracted from other
domains to solve several conflicting problems. Much of these methods have been adopted …

Swarm intelligence-based metaheuristic algorithms have attracted the attention of the
research community and have been exploited for effectively solving different optimization …

This paper presents a method for identifying various systems based on input-output data
and a real-coded genetic algorithm (RCGA). The advantages of this technique are, first, it is …

1 Background Numerous researchers have acknowledged the robustness and longevity of
genetic algorithm (GA) in tackling various problems, rendering them a potent optimisation …

The issue of insufficient samples usually occurs in real engineering problems because of the
time-consuming and expensive nature of collecting samples. In general, nonlinear modeling …

System identification is a method of determining a mathematical relation between variables
and terms of a process based on observed input-output data. Model structure selection is …

The calibration of the model is one of the most important steps in the development of models
of engineering systems. A new approach is presented in this study to calibrate a complex …

Two of the steps in system identification are model structure selection and parameter
estimation. In model structure selection, several model structures are evaluated and …